AI for help figuring things out and Timeshift for when you accidentally break something. One reboot and it’s fixed.
2,727 karma · joined December 30, 2012
If anything, we have more intractable problems needing deep creative solutions than ever before. People are dying as I write this. We’ve got mass displacement, poverty, polarization in politics. The education and healthcare systems are broken. Climate change marches on. Not to mention the social consequences of new technologies like AI (including the ones discussed in this post) that frankly no one knows what to do about.
The solution is indeed to work on bigger problems. If you can’t find any, look harder.
But as I understand the situation, even the major Deep Research systems still have this issue.
I expect the benefit is from better Skill design, specifically, minimizing the number of steps and decisions between the AI’s starting state and the correct information. Fewer transitions -> fewer chances for error to compound.
> 3 days
still seems slow! I’m saying what happens in 2028 when your entire project is 5-10 minutes of total agent runtime - time actually spent writing code and implementing your plan? Trying to parallelize 10m of work with a “town” of agents seems like unnecessary complexity.
Most of the time, the LLM’s framing of my idea is more generic and superficial than what I was actually getting at. It looks good, but when you look closer it often misses the point, on some level.
There is a real danger, to the extent you allow yourself to accept the LLM’s version of your idea, that you will lose the originality and uniqueness that made the idea interesting in the first place.
I think the struggle to frame a complex idea and the frustration that you feel when the right framing eludes you, is actually where most of the value is, and the LLM cheat code to skip past this pain is not really a good thing.
My understanding/experience is that LLM performance in a language scales with how well the language is represented in the training data.
From that assumption, we might expect LLMs to actually do better with an existing language for which more training code is available, even if that language is more complex and seems like it should be “harder” to understand.
It looked that way because they had media training and their public personas were carefully managed, with staged interviews and media appearances. Behind the scenes, it’s a different story.
Influencers are rewarded for seeming authentic. Mr Beast coming across badly in a traditional TV interview just makes his audience think he’s more real.
I still think there’s a third path, one that makes people’s lives better with thoughtful, respectful, and human-first use of AI. But for some reason there aren’t many people working on that.
https://www.farmersalmanac.com/end-of-an-era-farmers-almanac...
> This decision, though difficult, reflects the growing financial challenges of producing and distributing the Almanac in today’s chaotic media environment.
The ML field has a good understanding of the algorithms that produce these floating point numbers and lots of techniques that seem to produce “better” numbers in experiments. However, there is little to no understanding of what the numbers represent or how they do the things they do.
There were hints of where Microsoft was heading in Windows 10, but at least a lot of the worst “features” could be disabled.
I find 11 just completely unacceptable software to run on any system I own.